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Author Congcong Wang; Paul Nulty; David Lillis
Title Transformer-based Multi-task Learning for Disaster Tweet Categorisation Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 705-718
Keywords Disaster Response, Tweet Analysis, Transformers, Natural Language Processing
Abstract Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can contribute to the situational awareness of emergency responders, who have a need for them to be categorised according to information types (i.e. the type of aid services the messages are requesting). We introduce a transformer-based multi-task learning (MTL) technique for classifying information types and estimating the priority of these messages. We evaluate the effectiveness of our approach with a variety of metrics by submitting runs to the TREC Incident Streams (IS) track: a research initiative specifically designed for disaster tweet classification and prioritisation. The results demonstrate that our approach achieves competitive performance in most metrics as compared to other participating runs. Subsequently, we find that an ensemble approach combining disparate transformer encoders within our approach helps to improve the overall effectiveness to a significant extent, achieving state-of-the-art performance in almost every metric. We make the code publicly available so that our work can be reproduced and used as a baseline for the community for future work in this domain.
Address University College Dublin; University College Dublin; University College Dublin
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes wangcongcongcc@gmail.com Approved no
Call Number ISCRAM @ idladmin @ Serial 2366
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